Object Detection Model Based on Scene-Level Region Proposal Self-Attention

In order to improve the performance of two-stage object detection and consider the importance of scene and semantic information for visual recognition, the neural network of object detection algorithm is studied and analyzed in this paper. The main research work of this paper includes: A scene level region proposal self-attention object detection model based on depth separable convolution is proposed. In order to obtain stronger semantic information and context information of the target scene, the scene-level region proposal self-attention module is reconstructed based on the process of region proposal recognition. The feature map of the output feature pyramid network is sent into three parallel branches: semantic segmentation module, candidate area network module and region proposal self-attention module. At the same time, for the overall performance of the model, a deep separable convolutional network module is constructed on the backbone network, which includes six stages. In the fifth to sixth stage of the network, the separable convolutional network module is integrated respectively. Finally, a object detection method based on border regression network enhancement is proposed to achieve accurate target location. In order to verify the effectiveness of each model, the experimental results of each model are analyzed.